Text Classification
Transformers
PyTorch
ONNX
Tamil
English
multilingual
bert
Text Classification
text-embeddings-inference
Instructions to use seanbenhur/tanglish-offensive-language-identification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seanbenhur/tanglish-offensive-language-identification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="seanbenhur/tanglish-offensive-language-identification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("seanbenhur/tanglish-offensive-language-identification") model = AutoModelForSequenceClassification.from_pretrained("seanbenhur/tanglish-offensive-language-identification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from seanbenhur/tanglish-offensive-language-identification: direct link, hf CLI and curl.
- Browser
- Download file 950 MB
-
https://huggingface.co/seanbenhur/tanglish-offensive-language-identification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://seanbenhur/tanglish-offensive-language-identification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/seanbenhur/tanglish-offensive-language-identification/resolve/main/pytorch_model.bin
950 MB
- Xet hash:
- 1d83d2f6f6acff855c5a66195e049da03cda3afb5908af17255ffaa3e629e7a9
- Size of remote file:
- 950 MB
- SHA256:
- 87b0381aed37f6b0e017775ec8e616a8e5f5af51a2ca63e5f0a2532066fd1866
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